Conciseness

The body is efficient and assumes competence — no primer on what flame graphs are, and the category table carries skill-specific signature knowledge rather than generalities. It is not 5 because of trimmable repetition: the `sort -k2 -n -r folded.txt | head -5` command appears twice ("Reading the hot path" and the worked example), the "How to use" steps overlap the "Reading the hot path" section, and the same reference file is linked four times.

Actionability

Guidance is copy-paste ready end to end: a concrete sort command, `npx clinic flame -- node app.js` capture, real folded-stack sample output, a six-category signature table with specific frame names, and a fully rendered output template. It is not 4 because the worked example covers the common case completely and per-runtime capture commands are one reference away; the only trivially-derivable omission is how sample shares are computed.

Workflow Clarity

The five numbered steps are clearly sequenced and step 5 is an explicit verification checkpoint ("Re-profile after the change to confirm the delta, then hand off to perf-budget-gate"), with an expected outcome ("expect rank 1 to drop below 10%") in the worked example. It falls short of anchor 5 because error-recovery branching is not integrated into the workflow — remedies for a bad profile (hex addresses, unrepresentative sample) sit in Limitations/Anti-patterns rather than as feedback loops at the relevant step.

Progressive Disclosure

SKILL.md is a genuine overview: the core loop, classification table, and one worked example stay inline, while per-runtime capture wiring, the remediation catalog, and extended cases are clearly signaled and one level deep in the verified references/capturing-and-interpreting-flame-graphs.md. Content is appropriately split with no nested references, matching the anchor-5 structure.

Specificity

The description lists several concrete actions — "Reads flame graphs from py-spy, async-profiler, Go pprof or clinic.js, finds the hottest frames, tells CPU-bound from lock contention or allocation pressure and suggests the next step" — covering input, analysis, classification, and output. It stops short of anchor 5 because coverage has minor gaps: native perf/eBPF support and the reflection/logging/I/O-misclassification categories the skill actually handles are omitted, and "suggests the next step" is the least concrete action.

Completeness

Both halves are explicit: the "what" is the full action chain (reads flame graphs, finds hottest frames, distinguishes CPU-bound/lock/allocation, suggests next step) and the "when" opens with a concrete "Use when a performance regression is traced to a commit but the hot path inside it is unclear" trigger clause. This matches the anchor-5 example structure (what + explicit when with concrete triggers); it is not 4, where the when-clause would be generic or only partially explicit.

Trigger Term Quality

Natural phrases a user would say are present — "performance regression", "traced to a commit", "hot path", "flame graphs" — plus tool names (py-spy, async-profiler, Go pprof, clinic.js) users who have captured a profile would name. A few common natural terms are missing (e.g. "profile", "profiling", "CPU usage", "slow"), which keeps it at anchor 4 rather than 5's comprehensive synonym coverage.

Distinctiveness Conflict Risk

It carves a clear niche — flame-graph interpretation with named profilers and a specific trigger scenario (post-bisect regression triage) — so it is unlikely to fire for a generic profiling or code-review skill. The sibling perf skills it could neighbor (load testing, budget gating) target different stages of the workflow, so conflict risk is minimal.